Several methods have been proposed to address SSDA in semantic segmentation using deep learning. Wang et al. [1]} propose a semi-supervised approach termed "Alleviating Semantic-level Shift" (ASS) to promote distribution consistency between global and local views. However, supervision of labeled target samples cannot fully take advantage of labeled data in the two domains. Further, the adversarial loss used in this approach makes the training unstable due to weak supervision.
Chen et al. [2]} developed a framework based on a two-step domain adaptation technique that is realized using two complementary domain-mixed teachers from a holistic and partial views, and a student model for distilling knowledge from the two teachers. The cross-domain region-level mixing in this approach uses CutMix [3]} and so fails to respect object boundaries.
Intra-domain discrepancy in SSDA based semantic segmentation has not been addressed by these methods. Our proposed approach addresses these limitations and introduces a new perspective of intra-domain discrepancy within the target domain.
